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ITO multilayer films characteristics using neural network and genetic algorithm
Expert Systems with Applications 39 (2012) 8885–8889
Contents lists available at SciVerse ScienceDirect
Expert Systems with Applications journal homepage: www.elsevier.com/locate/eswa
Modeling and optimization of ITO/Al/ITO multilayer films characteristics using neural network and genetic algorithm Edward Namkyu Cho, Pyung Moon, Chang Eun Kim, Ilgu Yun ⇑ Department of Electrical and Electronic Engineering, 50 Yonsei-ro, Seodaemun-gu, Yonsei University, Seoul 120-749, Republic of Korea
a r t i c l e
i n f o
Keywords: ITO/Al/ITO Multilayer films Figure of merit Neural network Genetic algorithm
a b s t r a c t In this paper, ITO/Al/ITO multilayer films are fabricated with the variations of Al film thickness and annealing temperature. The effects of Al film thickness and annealing temperature on sheet resistance, optical transmittance, and the figure of merit are analyzed in the aid of the artificial neural network (NNet) models. In order to verify the fitness of NNet model, the root mean square error (RMSE) of training and testing data are calculated. The NNet models well represent the measured sheet resistance, optical transmittance, and the figure of merit. After NNet model is established, genetic algorithm (GA) is used to find the optimum process condition for the ITO/Al/ITO multilayer films to obtain maximum figure of merit in the design space. Ó 2012 Elsevier Ltd. All rights reserved.
1. Introduction Research on transparent conductive oxides (TCOs) has been quite emphasized in the recent years, largely due to an increasing demand for optoelectronic devices such as organic light emitting diodes (OLEDs), solar cells, and flat panel display (Bhosle et al., 2007). TCO films need to have low resistivity and high optical transmittance in the visible region. Among many TCO materials, tin doped indium oxide (ITO) is a well-known material for the use in TCO applications. However, the more improved structures of TCO films are required due to problems such as limited process temperature for the use on glass or polymer substrates (Park et al., 2009). To improve electrical, structural and optical properties, there have been researches on TCO/metal/TCO structures such as ITO/Cu/ITO (Park et al., 2009), ITO/Ag/ITO (Klöppel et al., 2000), ZnS/Ag/ZnS (Neghabi, Behjat, Ghorashi, & Salehi, 2011). A semiconductor manufacturing process generally exhibits the nonlinear characteristics due to the random fluctuations of the manufacturing, such as ambient contamination and unavoidable random variations of the process. Neural network (NNet) can perform complex mapping between input variables and output responses. NNet can generalize the whole tendencies of functional relationships from limited number of experimental data in the design space (May & Spanos, 2006). In this work, the investigations on sheet resistance, transmittance, and figure of merit for the ITO/Al/ITO multilayer films with the variations of Al film thickness and annealing temperature are performed with the help of NNet modeling methodology. After ⇑ Corresponding author. Tel.: +82 2 2123 4619; fax: +82 2 313 2879. E-mail address: [email protected] (I. Yun). 0957-4174/$ - see front matter Ó 2012 Elsevier Ltd. All rights reserved. doi:10.1016/j.eswa.2012.02.019
the NNet model is established, the genetic algorithm (GA), which is a global optimization algorithm, is used to explore the optimum process condition for the maximum figure of merit. 2. Experiments 2.1. Experimental details ITO and Al thin films were deposited on glass substrates by DC magnetron sputtering and evaporation system, respectively. Prior to the deposition, glass substrates were ultrasonically cleaned by acetone, isopropanol, de-ionized water and dried in nitrogen gas, subsequently. The ITO target (90 wt.% In2O3–10 wt.% SnO2) was used in sputtering. The chamber was initially evacuated to 5 105 Torr. ITO was deposited using Ar gas at the pressure of 5 mTorr with DC power fixed at 50 W. After sputtering, the samples were carried into evaporation chamber with the base pressure of 6 106 Torr. 3-mm diameter Al chips (99.9% purity) were then evaporated on the glass/ITO films. Finally, sputtering of the final ITO thin films was again performed. All deposition processes were performed in room temperature. The thicknesses of ITO and Al thin films were measured by spectroscopic ellipsometer and quartz crystal thickness controller, respectively. The each ITO film thickness was approximately 115 nm and Al film thickness was varied in the range of 2.5–10 nm. For comparison, ITO thin films without Al film layer were also deposited. After the deposition, ITO/Al/ITO multilayer films were thermally annealed in mixed gas atmosphere (90% N2–10% H2) for 3 min at maximum temperature of 200 °C. Sheet resistance of ITO/Al/ITO multilayer film was measured with the four point probe method. Optical transmittance was measured in the range of 300–800 nm by the UV–VIS spectrophotometer.
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Structural property of the multilayer film was examined with X-ray diffraction (XRD) method where Ni-filtered Ka (k ¼ 1:54056 Å) was used. 2.2. Modeling schemes Al film thickness and annealing temperature are selected as input variables to model the characteristics of ITO/Al/ITO multilayer films. The sheet resistance, optical transmittance, and figure of merit are defined as the output variables. Table 1 shows the experimental design matrix of ITO/Al/ITO multilayer films with 13 runs. The run order had been randomized to statistically avoid irrelevant factors that affect characteristics of ITO/Al/ITO multilayer films. NNet generally consist of input layer, hidden layer, and output layer where each layer is linked with neurons. Each link has a weight and bias which are modified during the modeling procedure. As a training algorithm, error back propagation is commonly used in NNet modeling of semiconductor process (Han & May, 1997; Venkateswaran, Rai, Govindan, & Meyyappan, 2002). Back propagation algorithm adjusts the weights of the NNet by using gradient descent approach in order to minimize the total error of the network. The 10 experiments have been trained by NNet. The additional 3 experiments have been used as training data to verify the predicted NNet models. For the verification of NNet model, the root mean square error (RMSE) of training and testing data are calculated. The RMSE is defined as the following equation (Mayers & Montgomery, 1995):
RMSE ¼
" !#1=2 n X 1 ðPi M i Þ2 n i¼1
ð1Þ
where n, Pi and Mi are number of data, ith predicted output and ith measured output, respectively. The NNet structure used in this work is 2-4-4-1 indicating 1 input layer, 2 hidden layers, and 1 output layer. The NNet learning rate and momentum coefficient are 0.015 and 0.03, respectively. GA which is one of the global searching methods is used after the NNet model for figure of merit is established. GA can effectively
Table 1 The experimental design matrix. Run
Al film thickness (nm)
Annealing temperature (°C)
Remark
1 2 3 4 5 6 7 8 9 10 11 12 13
2.5 7.5 5 7.5 10 10 7.5 2.5 5 5 2.5 10 2.5
0 0 100 200 200 0 100 200 200 0 150 100 50
Training
Testing
Table 2 GA parameters. Parameter Population size Population crossover Population mutation Chromosome length
17 0.95 0.01 100
find the optimum condition having the specified target response value (Ko et al., 2009). In this work, GA is used to find the optimum condition for maximum figure of merit in the experimental design space. GA operates in simple four stages which are creation of a population of strings, evaluation of each string, selection of string using the fitness value, and creation of new population of strings by genetic manipulation (Ko et al., 2009). GA parameters used in this work is summarized in Table 2. 3. Results and discussion NNet modeling results for the sheet resistance are shown in Fig. 1. The Fig. 1a shows the plot of residuals versus the run orders for sheet resistance. As can be seen from Fig. 1a, it is clearly shown that the residuals are randomly distributed having no specific patterns. Fig. 1b plots the measured vs. NNet predicted values where circles (‘s’) and squares (‘h’) represent training and testing data, respectively. The NNet model well reproduces measured sheet resistance results. The RMSE of training and testing data are 4.44 and 10.73, respectively. The surface response plot for sheet resistance is shown in Fig. 1c. It is shown that sheet resistance is the largest when Al film thickness is 2.5 nm with no annealing treatment. As Al film thickness or annealing temperature increases, the sheet resistance decreases. The decrease of sheet resistance as Al film thickness increases can be explained by the increase of carrier concentration (Leng et al., 2010). Considering Al work function (4.08 eV) and ITO work function (4.7 eV), electrons are easily injected from the Al layer to ITO layer by the insertion of Al film between the ITO films making the decrease of sheet resistance. In previous works regarding with TCO/metal/TCO multilayer films, it was reported that sheet resistance is decreased with the increase of annealing temperature due to the increase of carrier concentration on metal film (Neghabi et al., 2011). For comparison, the annealing effect on sheet resistance of ITO film without Al film is shown in Fig. 2. In Fig. 2, the sheet resistance of ITO film increases as annealing temperature increases which is opposite trend comparing with ITO/Al/ITO multilayer films. According to the report by Morikawa and Fujita (2000), the ITO film crystallizing temperature is approximately 165 °C. The sheet resistance begins to increase when the film structure changes from amorphous to crystalline structure (Lin, Chang, & Chiu, 2006). Similar results were shown in our annealing temperature range. It is worth to note that the sheet resistance of ITO/Al/ITO multilayer film when Al film thickness is 2.5 nm is larger than the film without annealing treatment. This can be resulted from the distinct island structure of Al film which similarly happens in metal film when thickness is too thin (Leng et al., 2010). However, the sheet resistance of ITO/Al/ ITO multilayer films rapidly decreases when the annealing treatment is executed. The total resistance of ITO/Al/ITO multilayer films can be easily described as:
1 1 1 1 ¼ þ þ RTot RITO RAl RITO
ð2Þ
where RTot, RITO, and RAl mean the total resistance of multilayer films, the resistance of ITO, and the resistance of Al, respectively. From Eq. (2), even though RITO increases when the annealing temperature increases, RAl has the large effect on decreasing RTot as the annealing temperature increases. Figs. 3a and b show the annealing effect on XRD patterns of the ITO films and ITO/Al(10 nm)/ITO multilayer films. From Fig. 3a, it is shown that the ITO film shows crystalline when the annealing temperature is 200 °C which is consistent with the result of Morikawa and Fujita (2000). In Fig. 3b, as the annealing temperature increases, Al film peak (1 1 1) intensity becomes larger indicating the enhancement of Al crystallinity. The enhancement of Al film
E.N. Cho et al. / Expert Systems with Applications 39 (2012) 8885–8889
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Fig. 2. Annealing effect on the sheet resistance of ITO film without Al film.
Fig. 1. NNet modeling results for sheet resistance with (a) residual plot, (b) the measured vs. NNet modeled data and (c) surface response plot.
crystallinity can attribute to the decrease of the sheet resistance due to the reduction of grain boundary scattering. The surface response plot for average transmittance is shown in Fig. 4. The RMSE of training and testing data are 5.07 and 5.25, respectively. The average transmittance is calculated by Leftheriotis, Yianoulis and Patrikios (1997):
R T av ¼
TðkÞf ðkÞdk R f ðkÞdk
ð3Þ
where f(k) is the luminous spectral efficiency and T(k) is the measured transmittance.
Fig. 3. Annealing effect on XRD patterns of (a) ITO films and (b) ITO/Al(10 nm)/ITO multilayer films.
It is shown that the average transmittance shows the peak value when the Al film thickness is near 2.5 nm and the annealing temperature is 200 °C. The average transmittance increases as the Al film thickness decreases or the annealing temperature increases. It is shown that the Al film thickness has larger effect than the annealing temperature on the average transmittance. Because Al is a metal which is naturally opaque, reflection increases as Al film
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E.N. Cho et al. / Expert Systems with Applications 39 (2012) 8885–8889
Fig. 6. Surface response plot for the figure of merit.
Fig. 4. Surface response plot for the average transmittance.
Table 3 Summary of the optimized GA result and measured result.
GA result Measured result
Al film thickness (nm)
Annealing temperature (°C)
Figure of merit (0.0001 X-1)
5.07 5
200 200
12.28 13.84
design range. The increase of transmittance was previously explained by the enhancement of metal layer crystallinity (Neghabi et al., 2011). As already mentioned in Fig. 3b, it is shown that Al crystallinity is enhanced as the annealing temperature increases. Fig. 5b shows the transmittance curves of ITO/Al(2.5 nm)/ITO multilayer films as a function of the annealing temperature. As the annealing temperature increases, the transmittance of the multilayer films increases. It is worth to note that absorption edge shifts to shorter wavelength as the annealing temperature increases indicating the increase of carrier concentration which is in coincidence of the sheet resistance results. The effect is known as BursteinMoss effect (Hamberg, Granqvist, Berggren, Sernelius, & Engström, 1984). For the practical application in TCO areas, the high transmittance and low sheet resistance are required. In order to estimate the quality of the multilayer films, the figure of merit is defined as Haacke (1976):
F TC ¼
Fig. 5. Transmittance curves of (a) as-deposited ITO/Al/ITO multilayer films as a function of Al film thickness and (b) ITO/Al(2.5 nm)/ITO multilayer films as a function of the annealing temperature.
thickness increases degrading the average transmittance of ITO/Al/ ITO multilayer film. The transmittance curves of the as-deposited ITO/Al/ITO multilayer films as a function of Al film thickness are shown in Fig. 5a. It is obviously shown that transmittance reduces as the Al film thickness increases. As the annealing temperature increases, the average transmittance increases in all experimental
T 10 av RS
ð4Þ
where RS is the sheet resistance. The surface response plot for the figure of merit is shown in Fig. 6. The RMSE of training and testing data are 1.04 and 1.68, respectively. The figure of merit is the highest when Al film thickness is in the range of 2.5–5 nm and annealing temperature is 200 °C. The figure of merit increases as the annealing temperature increases due to the reduction of sheet resistance and enhancement of transmittance. Even though the sheet resistance decreases as Al film thickness increases, the figure of merit decreases due to the rapid reduction of transmittance. From the combination effects of the average transmittance and the sheet resistance, there exists optimum condition for the maximum figure of merit. In order to find the optimum condition, GA is used and applied to the NNet model. The results for the optimized condition acquired from the GA and the measured data for comparison are summarized in Table 3. It is shown that the measured result is similar to the optimized GA result.
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4. Conclusion In this paper, the properties of ITO/Al/ITO multilayer films with the variations of Al film thickness and the annealing temperature were investigated. NNet modeling results were well matched with the measured data. As Al layer thickness or the annealing temperature was increased, the sheet resistance was decreased. The enhancement of optical transmittance was observed when Al layer thickness was decreased or the annealing temperature was increased. Due to the combination effects of the sheet resistance and the transmittance, there existed the optimum condition for the maximum figure of merit. From the GA optimization result, the maximum figure of merit was estimated to be 12.28 0.0001 X-1 when Al film thickness and the annealing temperature were 5.07 nm and 200 °C, respectively. The measured figure of merit was similar with the optimization result indicating that GA was proven to be an effective method to optimize the properties of ITO/Al/ITO multilayer films. Acknowledgment This work was supported as a research project of LG Display. References Bhosle, V., Prater, J. T., Yang, F., Burk, D., Forrest, S. R., & Narayan, J. (2007). Galliumdoped zinc oxide films as transparent electrodes for organic solar cell applications. Journal of Applied Physics, 102, 023501. Haacke, G. (1976). New figure of merit for transparent conductors. Journal of Applied Physics, 47, 4086.
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Hamberg, I., Granqvist, C. G., Berggren, K.-F., Sernelius, B. E., & Engström, L. (1984). Band-gap widening in heavily Sn-doped In2O3. Physical Review B, 30, 3240–3249. Han, S.-S., & May, G. S. (1997). Using neural network process models to perform PECVD silicon dioxide recipe synthesis via genetic algorithms. IEEE Transactions on Semiconductor Manufacturing, 10, 279–287. Klöppel, A., Kriegseis, W., Meyer, B. K., Scharmann, A., Daube, C., Stollenwerk, J., et al. (2000). Dependence of the electrical and optical behaviour of ITO-silver-ITO multilayers on the silver properties. Thin Solid Films, 365, 139–146. Ko, Y.-D., Moon, P., Kim, C. E., Ham, M.-H., Myoung, J.-M., & Yun, I. (2009). Modeling and optimization of the growth rate for ZnO thin films using neural networks and genetic algorithms. Expert Systems with Applications, 36, 4061–4066. Leftheriotis, G., Yianoulis, P., & Patrikios, D. (1997). Deposition and optical properties of optimised ZnS/Ag/ZnS thin films for energy saving applications. Thin Solid Films, 306, 92–99. Leng, J., Yu, Z., Xue, W., Zhang, T., Jiang, Y., Zhang, D., et al. (2010). Influence of Ag thickness on structural, optical, and electrical properties of ZnS/Ag/ZnS multilayers prepared by ion beam assisted deposition. Journal of Applied Physics, 108, 073109. Lin, T.-C., Chang, S.-C., & Chiu, C.-F. (2006). Annealing effect of ITO and ITO/Cu transparent conductive films in low pressure hydrogen atmosphere. Materials Science and Engineering B, 129, 39–42. May, G. S., & Spanos, C. J. (2006). Fundamentals of semiconductor manufacturing and process control. New Jersey: Wiley & Sons. Mayers, R. H., & Montgomery, D. C. (1995). Response surface methodology. New York: Wiely & Sons. Morikawa, H., & Fujita, M. (2000). Crystallization and electrical property change on the annealing of amorphous indium-oxide and indium-tin-oxide thin films. Thin Solid Films, 359, 61–67. Neghabi, M., Behjat, A., Ghorashi, S. M. B., & Salehi, S. M. A. (2011). The effect of annealing on structural, electrical and optical properties of nanostructured ZnS/ Ag/ZnS films. Thin Solid Films, 519, 5662–5666. Park, H. J., Park, J. H., Choi, J. I., Lee, J. Y., Chae, J. H., & Kim, D. (2009). Fabrication of transparent conductive films with a sandwich structure compose of ITO/Cu/ITO. Vaccum, 83, 448–450. Venkateswaran, S., Rai, M. M., Govindan, T. R., & Meyyappan, M. (2002). Neural network modeling of growth processes. Journal of the Electrochemical Society, 149, G137–G142.
Contents lists available at SciVerse ScienceDirect
Expert Systems with Applications journal homepage: www.elsevier.com/locate/eswa
Modeling and optimization of ITO/Al/ITO multilayer films characteristics using neural network and genetic algorithm Edward Namkyu Cho, Pyung Moon, Chang Eun Kim, Ilgu Yun ⇑ Department of Electrical and Electronic Engineering, 50 Yonsei-ro, Seodaemun-gu, Yonsei University, Seoul 120-749, Republic of Korea
a r t i c l e
i n f o
Keywords: ITO/Al/ITO Multilayer films Figure of merit Neural network Genetic algorithm
a b s t r a c t In this paper, ITO/Al/ITO multilayer films are fabricated with the variations of Al film thickness and annealing temperature. The effects of Al film thickness and annealing temperature on sheet resistance, optical transmittance, and the figure of merit are analyzed in the aid of the artificial neural network (NNet) models. In order to verify the fitness of NNet model, the root mean square error (RMSE) of training and testing data are calculated. The NNet models well represent the measured sheet resistance, optical transmittance, and the figure of merit. After NNet model is established, genetic algorithm (GA) is used to find the optimum process condition for the ITO/Al/ITO multilayer films to obtain maximum figure of merit in the design space. Ó 2012 Elsevier Ltd. All rights reserved.
1. Introduction Research on transparent conductive oxides (TCOs) has been quite emphasized in the recent years, largely due to an increasing demand for optoelectronic devices such as organic light emitting diodes (OLEDs), solar cells, and flat panel display (Bhosle et al., 2007). TCO films need to have low resistivity and high optical transmittance in the visible region. Among many TCO materials, tin doped indium oxide (ITO) is a well-known material for the use in TCO applications. However, the more improved structures of TCO films are required due to problems such as limited process temperature for the use on glass or polymer substrates (Park et al., 2009). To improve electrical, structural and optical properties, there have been researches on TCO/metal/TCO structures such as ITO/Cu/ITO (Park et al., 2009), ITO/Ag/ITO (Klöppel et al., 2000), ZnS/Ag/ZnS (Neghabi, Behjat, Ghorashi, & Salehi, 2011). A semiconductor manufacturing process generally exhibits the nonlinear characteristics due to the random fluctuations of the manufacturing, such as ambient contamination and unavoidable random variations of the process. Neural network (NNet) can perform complex mapping between input variables and output responses. NNet can generalize the whole tendencies of functional relationships from limited number of experimental data in the design space (May & Spanos, 2006). In this work, the investigations on sheet resistance, transmittance, and figure of merit for the ITO/Al/ITO multilayer films with the variations of Al film thickness and annealing temperature are performed with the help of NNet modeling methodology. After ⇑ Corresponding author. Tel.: +82 2 2123 4619; fax: +82 2 313 2879. E-mail address: [email protected] (I. Yun). 0957-4174/$ - see front matter Ó 2012 Elsevier Ltd. All rights reserved. doi:10.1016/j.eswa.2012.02.019
the NNet model is established, the genetic algorithm (GA), which is a global optimization algorithm, is used to explore the optimum process condition for the maximum figure of merit. 2. Experiments 2.1. Experimental details ITO and Al thin films were deposited on glass substrates by DC magnetron sputtering and evaporation system, respectively. Prior to the deposition, glass substrates were ultrasonically cleaned by acetone, isopropanol, de-ionized water and dried in nitrogen gas, subsequently. The ITO target (90 wt.% In2O3–10 wt.% SnO2) was used in sputtering. The chamber was initially evacuated to 5 105 Torr. ITO was deposited using Ar gas at the pressure of 5 mTorr with DC power fixed at 50 W. After sputtering, the samples were carried into evaporation chamber with the base pressure of 6 106 Torr. 3-mm diameter Al chips (99.9% purity) were then evaporated on the glass/ITO films. Finally, sputtering of the final ITO thin films was again performed. All deposition processes were performed in room temperature. The thicknesses of ITO and Al thin films were measured by spectroscopic ellipsometer and quartz crystal thickness controller, respectively. The each ITO film thickness was approximately 115 nm and Al film thickness was varied in the range of 2.5–10 nm. For comparison, ITO thin films without Al film layer were also deposited. After the deposition, ITO/Al/ITO multilayer films were thermally annealed in mixed gas atmosphere (90% N2–10% H2) for 3 min at maximum temperature of 200 °C. Sheet resistance of ITO/Al/ITO multilayer film was measured with the four point probe method. Optical transmittance was measured in the range of 300–800 nm by the UV–VIS spectrophotometer.
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E.N. Cho et al. / Expert Systems with Applications 39 (2012) 8885–8889
Structural property of the multilayer film was examined with X-ray diffraction (XRD) method where Ni-filtered Ka (k ¼ 1:54056 Å) was used. 2.2. Modeling schemes Al film thickness and annealing temperature are selected as input variables to model the characteristics of ITO/Al/ITO multilayer films. The sheet resistance, optical transmittance, and figure of merit are defined as the output variables. Table 1 shows the experimental design matrix of ITO/Al/ITO multilayer films with 13 runs. The run order had been randomized to statistically avoid irrelevant factors that affect characteristics of ITO/Al/ITO multilayer films. NNet generally consist of input layer, hidden layer, and output layer where each layer is linked with neurons. Each link has a weight and bias which are modified during the modeling procedure. As a training algorithm, error back propagation is commonly used in NNet modeling of semiconductor process (Han & May, 1997; Venkateswaran, Rai, Govindan, & Meyyappan, 2002). Back propagation algorithm adjusts the weights of the NNet by using gradient descent approach in order to minimize the total error of the network. The 10 experiments have been trained by NNet. The additional 3 experiments have been used as training data to verify the predicted NNet models. For the verification of NNet model, the root mean square error (RMSE) of training and testing data are calculated. The RMSE is defined as the following equation (Mayers & Montgomery, 1995):
RMSE ¼
" !#1=2 n X 1 ðPi M i Þ2 n i¼1
ð1Þ
where n, Pi and Mi are number of data, ith predicted output and ith measured output, respectively. The NNet structure used in this work is 2-4-4-1 indicating 1 input layer, 2 hidden layers, and 1 output layer. The NNet learning rate and momentum coefficient are 0.015 and 0.03, respectively. GA which is one of the global searching methods is used after the NNet model for figure of merit is established. GA can effectively
Table 1 The experimental design matrix. Run
Al film thickness (nm)
Annealing temperature (°C)
Remark
1 2 3 4 5 6 7 8 9 10 11 12 13
2.5 7.5 5 7.5 10 10 7.5 2.5 5 5 2.5 10 2.5
0 0 100 200 200 0 100 200 200 0 150 100 50
Training
Testing
Table 2 GA parameters. Parameter Population size Population crossover Population mutation Chromosome length
17 0.95 0.01 100
find the optimum condition having the specified target response value (Ko et al., 2009). In this work, GA is used to find the optimum condition for maximum figure of merit in the experimental design space. GA operates in simple four stages which are creation of a population of strings, evaluation of each string, selection of string using the fitness value, and creation of new population of strings by genetic manipulation (Ko et al., 2009). GA parameters used in this work is summarized in Table 2. 3. Results and discussion NNet modeling results for the sheet resistance are shown in Fig. 1. The Fig. 1a shows the plot of residuals versus the run orders for sheet resistance. As can be seen from Fig. 1a, it is clearly shown that the residuals are randomly distributed having no specific patterns. Fig. 1b plots the measured vs. NNet predicted values where circles (‘s’) and squares (‘h’) represent training and testing data, respectively. The NNet model well reproduces measured sheet resistance results. The RMSE of training and testing data are 4.44 and 10.73, respectively. The surface response plot for sheet resistance is shown in Fig. 1c. It is shown that sheet resistance is the largest when Al film thickness is 2.5 nm with no annealing treatment. As Al film thickness or annealing temperature increases, the sheet resistance decreases. The decrease of sheet resistance as Al film thickness increases can be explained by the increase of carrier concentration (Leng et al., 2010). Considering Al work function (4.08 eV) and ITO work function (4.7 eV), electrons are easily injected from the Al layer to ITO layer by the insertion of Al film between the ITO films making the decrease of sheet resistance. In previous works regarding with TCO/metal/TCO multilayer films, it was reported that sheet resistance is decreased with the increase of annealing temperature due to the increase of carrier concentration on metal film (Neghabi et al., 2011). For comparison, the annealing effect on sheet resistance of ITO film without Al film is shown in Fig. 2. In Fig. 2, the sheet resistance of ITO film increases as annealing temperature increases which is opposite trend comparing with ITO/Al/ITO multilayer films. According to the report by Morikawa and Fujita (2000), the ITO film crystallizing temperature is approximately 165 °C. The sheet resistance begins to increase when the film structure changes from amorphous to crystalline structure (Lin, Chang, & Chiu, 2006). Similar results were shown in our annealing temperature range. It is worth to note that the sheet resistance of ITO/Al/ITO multilayer film when Al film thickness is 2.5 nm is larger than the film without annealing treatment. This can be resulted from the distinct island structure of Al film which similarly happens in metal film when thickness is too thin (Leng et al., 2010). However, the sheet resistance of ITO/Al/ ITO multilayer films rapidly decreases when the annealing treatment is executed. The total resistance of ITO/Al/ITO multilayer films can be easily described as:
1 1 1 1 ¼ þ þ RTot RITO RAl RITO
ð2Þ
where RTot, RITO, and RAl mean the total resistance of multilayer films, the resistance of ITO, and the resistance of Al, respectively. From Eq. (2), even though RITO increases when the annealing temperature increases, RAl has the large effect on decreasing RTot as the annealing temperature increases. Figs. 3a and b show the annealing effect on XRD patterns of the ITO films and ITO/Al(10 nm)/ITO multilayer films. From Fig. 3a, it is shown that the ITO film shows crystalline when the annealing temperature is 200 °C which is consistent with the result of Morikawa and Fujita (2000). In Fig. 3b, as the annealing temperature increases, Al film peak (1 1 1) intensity becomes larger indicating the enhancement of Al crystallinity. The enhancement of Al film
E.N. Cho et al. / Expert Systems with Applications 39 (2012) 8885–8889
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Fig. 2. Annealing effect on the sheet resistance of ITO film without Al film.
Fig. 1. NNet modeling results for sheet resistance with (a) residual plot, (b) the measured vs. NNet modeled data and (c) surface response plot.
crystallinity can attribute to the decrease of the sheet resistance due to the reduction of grain boundary scattering. The surface response plot for average transmittance is shown in Fig. 4. The RMSE of training and testing data are 5.07 and 5.25, respectively. The average transmittance is calculated by Leftheriotis, Yianoulis and Patrikios (1997):
R T av ¼
TðkÞf ðkÞdk R f ðkÞdk
ð3Þ
where f(k) is the luminous spectral efficiency and T(k) is the measured transmittance.
Fig. 3. Annealing effect on XRD patterns of (a) ITO films and (b) ITO/Al(10 nm)/ITO multilayer films.
It is shown that the average transmittance shows the peak value when the Al film thickness is near 2.5 nm and the annealing temperature is 200 °C. The average transmittance increases as the Al film thickness decreases or the annealing temperature increases. It is shown that the Al film thickness has larger effect than the annealing temperature on the average transmittance. Because Al is a metal which is naturally opaque, reflection increases as Al film
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Fig. 6. Surface response plot for the figure of merit.
Fig. 4. Surface response plot for the average transmittance.
Table 3 Summary of the optimized GA result and measured result.
GA result Measured result
Al film thickness (nm)
Annealing temperature (°C)
Figure of merit (0.0001 X-1)
5.07 5
200 200
12.28 13.84
design range. The increase of transmittance was previously explained by the enhancement of metal layer crystallinity (Neghabi et al., 2011). As already mentioned in Fig. 3b, it is shown that Al crystallinity is enhanced as the annealing temperature increases. Fig. 5b shows the transmittance curves of ITO/Al(2.5 nm)/ITO multilayer films as a function of the annealing temperature. As the annealing temperature increases, the transmittance of the multilayer films increases. It is worth to note that absorption edge shifts to shorter wavelength as the annealing temperature increases indicating the increase of carrier concentration which is in coincidence of the sheet resistance results. The effect is known as BursteinMoss effect (Hamberg, Granqvist, Berggren, Sernelius, & Engström, 1984). For the practical application in TCO areas, the high transmittance and low sheet resistance are required. In order to estimate the quality of the multilayer films, the figure of merit is defined as Haacke (1976):
F TC ¼
Fig. 5. Transmittance curves of (a) as-deposited ITO/Al/ITO multilayer films as a function of Al film thickness and (b) ITO/Al(2.5 nm)/ITO multilayer films as a function of the annealing temperature.
thickness increases degrading the average transmittance of ITO/Al/ ITO multilayer film. The transmittance curves of the as-deposited ITO/Al/ITO multilayer films as a function of Al film thickness are shown in Fig. 5a. It is obviously shown that transmittance reduces as the Al film thickness increases. As the annealing temperature increases, the average transmittance increases in all experimental
T 10 av RS
ð4Þ
where RS is the sheet resistance. The surface response plot for the figure of merit is shown in Fig. 6. The RMSE of training and testing data are 1.04 and 1.68, respectively. The figure of merit is the highest when Al film thickness is in the range of 2.5–5 nm and annealing temperature is 200 °C. The figure of merit increases as the annealing temperature increases due to the reduction of sheet resistance and enhancement of transmittance. Even though the sheet resistance decreases as Al film thickness increases, the figure of merit decreases due to the rapid reduction of transmittance. From the combination effects of the average transmittance and the sheet resistance, there exists optimum condition for the maximum figure of merit. In order to find the optimum condition, GA is used and applied to the NNet model. The results for the optimized condition acquired from the GA and the measured data for comparison are summarized in Table 3. It is shown that the measured result is similar to the optimized GA result.
E.N. Cho et al. / Expert Systems with Applications 39 (2012) 8885–8889
4. Conclusion In this paper, the properties of ITO/Al/ITO multilayer films with the variations of Al film thickness and the annealing temperature were investigated. NNet modeling results were well matched with the measured data. As Al layer thickness or the annealing temperature was increased, the sheet resistance was decreased. The enhancement of optical transmittance was observed when Al layer thickness was decreased or the annealing temperature was increased. Due to the combination effects of the sheet resistance and the transmittance, there existed the optimum condition for the maximum figure of merit. From the GA optimization result, the maximum figure of merit was estimated to be 12.28 0.0001 X-1 when Al film thickness and the annealing temperature were 5.07 nm and 200 °C, respectively. The measured figure of merit was similar with the optimization result indicating that GA was proven to be an effective method to optimize the properties of ITO/Al/ITO multilayer films. Acknowledgment This work was supported as a research project of LG Display. References Bhosle, V., Prater, J. T., Yang, F., Burk, D., Forrest, S. R., & Narayan, J. (2007). Galliumdoped zinc oxide films as transparent electrodes for organic solar cell applications. Journal of Applied Physics, 102, 023501. Haacke, G. (1976). New figure of merit for transparent conductors. Journal of Applied Physics, 47, 4086.
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Hamberg, I., Granqvist, C. G., Berggren, K.-F., Sernelius, B. E., & Engström, L. (1984). Band-gap widening in heavily Sn-doped In2O3. Physical Review B, 30, 3240–3249. Han, S.-S., & May, G. S. (1997). Using neural network process models to perform PECVD silicon dioxide recipe synthesis via genetic algorithms. IEEE Transactions on Semiconductor Manufacturing, 10, 279–287. Klöppel, A., Kriegseis, W., Meyer, B. K., Scharmann, A., Daube, C., Stollenwerk, J., et al. (2000). Dependence of the electrical and optical behaviour of ITO-silver-ITO multilayers on the silver properties. Thin Solid Films, 365, 139–146. Ko, Y.-D., Moon, P., Kim, C. E., Ham, M.-H., Myoung, J.-M., & Yun, I. (2009). Modeling and optimization of the growth rate for ZnO thin films using neural networks and genetic algorithms. Expert Systems with Applications, 36, 4061–4066. Leftheriotis, G., Yianoulis, P., & Patrikios, D. (1997). Deposition and optical properties of optimised ZnS/Ag/ZnS thin films for energy saving applications. Thin Solid Films, 306, 92–99. Leng, J., Yu, Z., Xue, W., Zhang, T., Jiang, Y., Zhang, D., et al. (2010). Influence of Ag thickness on structural, optical, and electrical properties of ZnS/Ag/ZnS multilayers prepared by ion beam assisted deposition. Journal of Applied Physics, 108, 073109. Lin, T.-C., Chang, S.-C., & Chiu, C.-F. (2006). Annealing effect of ITO and ITO/Cu transparent conductive films in low pressure hydrogen atmosphere. Materials Science and Engineering B, 129, 39–42. May, G. S., & Spanos, C. J. (2006). Fundamentals of semiconductor manufacturing and process control. New Jersey: Wiley & Sons. Mayers, R. H., & Montgomery, D. C. (1995). Response surface methodology. New York: Wiely & Sons. Morikawa, H., & Fujita, M. (2000). Crystallization and electrical property change on the annealing of amorphous indium-oxide and indium-tin-oxide thin films. Thin Solid Films, 359, 61–67. Neghabi, M., Behjat, A., Ghorashi, S. M. B., & Salehi, S. M. A. (2011). The effect of annealing on structural, electrical and optical properties of nanostructured ZnS/ Ag/ZnS films. Thin Solid Films, 519, 5662–5666. Park, H. J., Park, J. H., Choi, J. I., Lee, J. Y., Chae, J. H., & Kim, D. (2009). Fabrication of transparent conductive films with a sandwich structure compose of ITO/Cu/ITO. Vaccum, 83, 448–450. Venkateswaran, S., Rai, M. M., Govindan, T. R., & Meyyappan, M. (2002). Neural network modeling of growth processes. Journal of the Electrochemical Society, 149, G137–G142.